Text Classification
Transformers
Safetensors
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use Dreamer-O/my_awesome_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Dreamer-O/my_awesome_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Dreamer-O/my_awesome_model")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Dreamer-O/my_awesome_model") model = AutoModelForSequenceClassification.from_pretrained("Dreamer-O/my_awesome_model") - Notebooks
- Google Colab
- Kaggle
my_awesome_model
This model is a fine-tuned version of distilbert/distilbert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.2339
- Accuracy: 0.9313
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 2
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.2206 | 1.0 | 1563 | 0.1946 | 0.9248 |
| 0.1424 | 2.0 | 3126 | 0.2339 | 0.9313 |
Framework versions
- Transformers 4.57.6
- Pytorch 2.9.1
- Datasets 4.5.0
- Tokenizers 0.22.2
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Model tree for Dreamer-O/my_awesome_model
Base model
distilbert/distilbert-base-uncased